Cost savings of implementing the SickKids Paediatric Orthopaedic Pathway for proximal humerus fractures in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The SickKids Paediatric Orthopaedic Pathway (SKPOP) for proximal humerus fractures may safely reduce the number of radiographs and follow-up assessments for children with these injuries. The study objective was to examine potential cost-savings of the SKPOP from the perspective of the Ministry of Health and Long-term Care (MOHLTC). METHODS: Two sets of resource profiles, based on direct health care costs were created for a cohort of patients treated at our institution: the first based on actual follow-up assessment values, and the other based on follow-up assessments according to the SKPOP. Differences between the two profiles represent potential cost-savings. A decision-analysis and associated probabilistic sensitivity analysis (PSA) were performed. RESULTS: In a cohort of 239 patients treated between 2009 and 2014, 92.9% (222) would have met SKPOP eligibility. Management according to this pathway would have reduced orthopaedic assessments and shoulder radiograph series by 83.6% (470/562) and 70.8% (367/589), respectively. For the cohort examined, a potential cost-savings of $30,040.56 ($135.32/patient) was observed. A PSA, accounting for variable SKPOP adherence and health care utilization, yielded cost-savings in 96.5% of the iterations run through the decision-analysis model and an average cost-savings of $57.82/patient. Based on these results and the annual provincial incidence rate of eligible patients (n=575), the MOHLTC could potentially save $33,249.45 annually with province-wide implementation. CONCLUSIONS: Implementation of the SKPOP for a cohort of patients managed at our institution could have resulted in cost-savings due to substantial reductions in health care utilization. Cost-savings are likely to occur with provincial implementation of the SKPOP for proximal humerus fractures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".